Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | https://www.revistas.usp.br/sa/article/view/183239 |
Resumo: | Multicollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses. |
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Scientia Agrícola (Online) |
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Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearitySEM methodologytrail crestcorrelationMulticollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2021-01-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/18323910.1590/1678-992X-2019-0081Scientia Agricola; v. 78 n. 2 (2021); e20190081Scientia Agricola; Vol. 78 No. 2 (2021); e20190081Scientia Agricola; Vol. 78 Núm. 2 (2021); e201900811678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/183239/169919Copyright (c) 2021 Scientia Agricolahttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessSilva, Flavia Alves da Correa, Caio Cezar Guedes Carvalho, Beatriz Murizini Viana , Alexandre Pio Preisigke, Sandra da Costa Amaral Júnior, Antônio Teixeira do 2021-03-18T18:32:16Zoai:revistas.usp.br:article/183239Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2021-03-18T18:32:16Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity |
title |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity |
spellingShingle |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity Silva, Flavia Alves da SEM methodology trail crest correlation |
title_short |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity |
title_full |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity |
title_fullStr |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity |
title_full_unstemmed |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity |
title_sort |
Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity |
author |
Silva, Flavia Alves da |
author_facet |
Silva, Flavia Alves da Correa, Caio Cezar Guedes Carvalho, Beatriz Murizini Viana , Alexandre Pio Preisigke, Sandra da Costa Amaral Júnior, Antônio Teixeira do |
author_role |
author |
author2 |
Correa, Caio Cezar Guedes Carvalho, Beatriz Murizini Viana , Alexandre Pio Preisigke, Sandra da Costa Amaral Júnior, Antônio Teixeira do |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Silva, Flavia Alves da Correa, Caio Cezar Guedes Carvalho, Beatriz Murizini Viana , Alexandre Pio Preisigke, Sandra da Costa Amaral Júnior, Antônio Teixeira do |
dc.subject.por.fl_str_mv |
SEM methodology trail crest correlation |
topic |
SEM methodology trail crest correlation |
description |
Multicollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-06 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistas.usp.br/sa/article/view/183239 10.1590/1678-992X-2019-0081 |
url |
https://www.revistas.usp.br/sa/article/view/183239 |
identifier_str_mv |
10.1590/1678-992X-2019-0081 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.revistas.usp.br/sa/article/view/183239/169919 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Scientia Agricola http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Scientia Agricola http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz |
publisher.none.fl_str_mv |
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz |
dc.source.none.fl_str_mv |
Scientia Agricola; v. 78 n. 2 (2021); e20190081 Scientia Agricola; Vol. 78 No. 2 (2021); e20190081 Scientia Agricola; Vol. 78 Núm. 2 (2021); e20190081 1678-992X 0103-9016 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1787713262218903552 |